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Cliffs, Spikes and Valleys

Cliffs, Spikes and Valleys. Goetz Graefe and Harumi Kuno. 50% of power is load-independent. Clearly we need workload optimization: Increase server utilization Decrease power wasted by idle machines Without sacrificing performance.  So what’s the problem?

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Cliffs, Spikes and Valleys

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  1. Cliffs, Spikes and Valleys Goetz Graefe and Harumi Kuno

  2. 50% of power is load-independent • Clearly we need workload optimization: • Increase server utilization • Decrease power wasted by idle machines • Without sacrificing performance.  • So what’s the problem? • Hard to predict cliffs, spikes, and valleys in mixed workloads • Hard to plan  Safer to over-provision

  3. Scan one index, apply 2nd predicate Example: System B has a cliff System A System B

  4. Example: contention moves cliffs overflow

  5. Example valley and spike: online index creation Load Combined load System capacity Query load Indexing load Time SMDB 2011 - Hannover

  6. For example: prevent a spike by accommodating a valley Load System capacity Query load Delayed indexcompletion Time SMDB 2011 - Hannover

  7. How to motivate, evaluate, protect improvements in robust query processing? • Metrics for “good performance every time” • As opposed to optimal performance, predictability or progress estimation. • Metric that rewards lack of cliffs, spikes, and valleys, • Not just predict them • Improvements include: • Robust algorithms (e.g., memory-adaptive sorting) • Robust algorithm choices (e.g., g-join and g-distinct) • Robust plans (e.g., dynamic re-ordering of operations within a plan), etc.

  8. Nutshell • How can (data management) software prevent over-provisioning? • Workload management and optimization : great, but what about the cliffs, spikes, and valleys? • What are good metrics and tests for “good performance every time” ? • How can we improve query processing robustness?

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